class_docstr: Abstract container mirroring Sklearn regressors API.
docstr: Utility functions used to emulate the behavior of the Sklearn API.
  On regression returns the predicted values.
  On classification tasks returns the predicted class labels for the input data.
  On anomaly detection (e.g. isolation forest) returns the predicted classes (-1 or 1).
docstr_tok: ["Utility","functions","used","to","emulate","the","behavior","of","the","Sklearn","API",".","On","regression","returns","the","predicted","values",".","On","classification","tasks","returns","the","predicted","class","labels","for","the","input","data",".","On","anomaly","detection","(","e.g",".","isolation","forest",")","returns","the","predicted","classes","(","-1","or","1",")","."]
code: def predict(self, *inputs):
      """
      Utility functions used to emulate the behavior of the Sklearn API.
      On regression returns the predicted values.
      On classification tasks returns the predicted class labels for the input data.
      On anomaly detection (e.g. isolation forest) returns the predicted classes (-1 or 1).
      """
      return self._run(self._predict, *inputs)
  
imports: from abc import ABC, abstractmethod
  from hummingbird.ml._utils import pandas_installed
  from hummingbird.ml.operator_converters import constants
label_desc: Use image node_id 3 for calling the SklearnContainerRegression obj's underlying member method code with example usage: obj.predict() and returns: self